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The effect of camera settings on image noise and accuracy of subpixel image registration

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Abstract

Estimating the accuracy of subpixel image registration algorithms in practical experiments is important, yet challenging in many applications. The accuracy of these algorithms in camera-based systems is influenced not only by image content, but also camera noise. In this study, five experiments were designed to quantify the effect of changing camera settings on the image noise level, and consequently on the accuracy of subpixel displacement measurements. The systematic errors of the algorithms were measured separately to provide estimates of their contributions to the total measurement error. Experiments were conducted to measure the effects of random noise on the accuracy of displacement measurements. The results showed that the camera parameters directly influenced the image noise level, but the accuracy of algorithms in measuring displacements was not proportional to the image noise level. These results provide information on the quantitative relationship between camera settings and the registration error for a camera-based measurement system.

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Acknowledgements

The authors acknowledge the financial support from the New Zealand Government National Science Challenge for Technological Innovation, and the University of Auckland Foundation.

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Authors and Affiliations

  1. Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand

    Amir HajiRassouliha, Samuel P. Richardson, Andrew J. Taberner, Martyn P. Nash & Poul M. F. Nielsen

  2. Department of Engineering Science, The University of Auckland, Auckland, New Zealand

    Andrew J. Taberner, Martyn P. Nash & Poul M. F. Nielsen

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  1. Amir HajiRassouliha
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  2. Samuel P. Richardson
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  3. Andrew J. Taberner
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  4. Martyn P. Nash
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  5. Poul M. F. Nielsen
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Correspondence to Amir HajiRassouliha.

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HajiRassouliha, A., Richardson, S.P., Taberner, A.J. et al. The effect of camera settings on image noise and accuracy of subpixel image registration. Machine Vision and Applications 32, 95 (2021). https://doi.org/10.1007/s00138-021-01215-4

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  • DOI: https://doi.org/10.1007/s00138-021-01215-4

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